The data obtained from a high resolution seismic refraction profile, which was carded out in Jiashi, Xinjiang, strong earthquake swarm area, were processed with both finite difference inversion and Hagedoorn refractor...The data obtained from a high resolution seismic refraction profile, which was carded out in Jiashi, Xinjiang, strong earthquake swarm area, were processed with both finite difference inversion and Hagedoorn refractor wavefront imaging technique and the fine upper crustal structure was determined. The results show that the upper crustal structure is relatively well-distributed in laterally and obviously by layers vertically.From surface to 11.0 km depth, there are about four layers. The P wave velocity of top two layers range from 1.65 to 4.5 km/s and their bottom boundaries, the buried depths of which are 0.4, 2.96-3.0 km respectively, are almost horizontal; The third layer is comparatively complicated and its P wave velocity presents inhomogeneous in both laterally and vertically. The bottom boundary of third layer is crystalline basement and shows a little uplift, which seemly suggest that the upper crust had been resisted while the hard Tarim block inserting into Tianshan Mountain; The forth layer is relatively even and its P wave velocity is about 6.3 km/s. There are a lateral velocity variation at the depth of about 4.0 km, and suggest that it has something to do with the hidden Meigaiti fault and Meigaiti-Xiasuhong fault but there are no the structure features about these faults stretching to the surface and passing through the crystalline basement. The seismogenic tectonic of Jiashi strong earthquake swarm at least lies in middle or lower crust beneath 11.0 km depth.展开更多
Land use and land cover(LULC)has undergone drastic changes with the rapid growth of the global population,economic development,and the expansion of agricultural activities.However,the uncertainty of classification alg...Land use and land cover(LULC)has undergone drastic changes with the rapid growth of the global population,economic development,and the expansion of agricultural activities.However,the uncertainty of classification algorithms and image resolution based on satellite data for land cover mapping,particularly cropland cover mapping,needs to be investigated sufficiently.In this study,the influence of different spatial-resolution images on classification results was explored by comparing the differences between four machine learning algorithms for LULC mapping.The classification results of this model were also compared with existing global land cover datasets to determine whether the model was capable of producing reliable results.According to the results of this study,the random forest(RF)classifier outperformed the support vector machine(SVM),decision tree(DT),and artificial neural network(ANN)with an overall accuracy(OA)and kappa coefficient of 81.99%and 0.78,respectively.However,SVM and ANN showed greater accuracy on the water class and unused land class,respectively.With increasing spatial resolution,RF’s accuracy increased initially and then decreased when classifying images with five different spatial resolutions(30 m,16 m,10 m,8 m,and 2 m).In particular,with an OA of 82.54%and a kappa coefficient of 0.78,RF performed the best on images with 8 m resolution.Additionally,the RF-based image with 8 m resolution produced a higher OA of 0.88 for cropland.Topography is the main factor that determines the classification performance of different-resolution images.The classification accuracies of RF10 m and RF30 m(10 m and 30 m resolution images,respectively,using RF)were higher(OAs of 93.59%and 94.59%,respectively)than those of the global land cover dataset(LC10 m and LC30 m,land cover images with 10 m and 30 m resolution,respectively),whose high-resolution images showed more details of the land cover.The results of this study highlight that classification algorithms and image resolution are the sources of uncertainty for land mapping.Obtaining reliable land cover mapping requires the use of appropriate classification algorithms and spatial resolution.With these results,it will be possible to develop a national land monitoring system and basic ecological climate models using LULC.展开更多
As the use of Cannabis products as natural medicines burgeons,it is also appearing as a food ingredient.It is important to screen Cannabis samples as ingredients by profiling their chemical compositions,which is refer...As the use of Cannabis products as natural medicines burgeons,it is also appearing as a food ingredient.It is important to screen Cannabis samples as ingredients by profiling their chemical compositions,which is referred to as chemotyping.Two sets of botanical extracts were studied.The first set is referred to as Cannabis contained plant materials from 15 samples of the sativa,indica,and hybrids of the two species.The second set contained 20 extracts from the variety of Cannabis sativa with low tetrahydrocannabinol(THC)concentrations,i.e.,below 0.3%,and,henceforth,will be referred to as hemp.An ultraviolet(UV)microplate reader provides a cost-effective and high-throughput method for identifying chemotypes of plant extracts by their spectra.The microplate reader affords rapid measurements of small volumes,e.g.,50μL,which demonstrates a potential to significantly reduce the analysis time and cost for Cannabis and hemp chemotyping or chemi-cal profiling.Replicate samples were measured on different days to demonstrate the robustness of the method.Projected difference resolution(PDR)maps were used to visualize the separations among the classes.Five multivariate classifiers,fuzzy rule-building expert system(FuRES),super partial least squares-discriminant analysis(sPLS-DA),support vector machine(SVM),and two tree-based support vector machines(SVMtreeG and SVMtreeH)were evaluated.The classifiers were validated with ten bootstrapped Latin partitions(BLPs).For the Cannabis extracts,the SVMtreeG yielded the best performance and the classification accuracy was 99.1±0.4%for spectra collected in the nonlinear absorbance range.For the hemp extracts,the SVM classifier performed the best with a 97.4±0.6%classification accuracy.These results demonstrate that the UV microplate reader coupled with multivariate classifiers can be used as a high-throughput and cost-effective approach for chemotyping Cannabis.展开更多
基金National Natural Science Foundation of China (40334040) and Joint Seismological Foundation (106076).
文摘The data obtained from a high resolution seismic refraction profile, which was carded out in Jiashi, Xinjiang, strong earthquake swarm area, were processed with both finite difference inversion and Hagedoorn refractor wavefront imaging technique and the fine upper crustal structure was determined. The results show that the upper crustal structure is relatively well-distributed in laterally and obviously by layers vertically.From surface to 11.0 km depth, there are about four layers. The P wave velocity of top two layers range from 1.65 to 4.5 km/s and their bottom boundaries, the buried depths of which are 0.4, 2.96-3.0 km respectively, are almost horizontal; The third layer is comparatively complicated and its P wave velocity presents inhomogeneous in both laterally and vertically. The bottom boundary of third layer is crystalline basement and shows a little uplift, which seemly suggest that the upper crust had been resisted while the hard Tarim block inserting into Tianshan Mountain; The forth layer is relatively even and its P wave velocity is about 6.3 km/s. There are a lateral velocity variation at the depth of about 4.0 km, and suggest that it has something to do with the hidden Meigaiti fault and Meigaiti-Xiasuhong fault but there are no the structure features about these faults stretching to the surface and passing through the crystalline basement. The seismogenic tectonic of Jiashi strong earthquake swarm at least lies in middle or lower crust beneath 11.0 km depth.
基金supported by the Natural Science Research General Program of Shanxi Province Basic Research Project(Grant No.202203021221231).
文摘Land use and land cover(LULC)has undergone drastic changes with the rapid growth of the global population,economic development,and the expansion of agricultural activities.However,the uncertainty of classification algorithms and image resolution based on satellite data for land cover mapping,particularly cropland cover mapping,needs to be investigated sufficiently.In this study,the influence of different spatial-resolution images on classification results was explored by comparing the differences between four machine learning algorithms for LULC mapping.The classification results of this model were also compared with existing global land cover datasets to determine whether the model was capable of producing reliable results.According to the results of this study,the random forest(RF)classifier outperformed the support vector machine(SVM),decision tree(DT),and artificial neural network(ANN)with an overall accuracy(OA)and kappa coefficient of 81.99%and 0.78,respectively.However,SVM and ANN showed greater accuracy on the water class and unused land class,respectively.With increasing spatial resolution,RF’s accuracy increased initially and then decreased when classifying images with five different spatial resolutions(30 m,16 m,10 m,8 m,and 2 m).In particular,with an OA of 82.54%and a kappa coefficient of 0.78,RF performed the best on images with 8 m resolution.Additionally,the RF-based image with 8 m resolution produced a higher OA of 0.88 for cropland.Topography is the main factor that determines the classification performance of different-resolution images.The classification accuracies of RF10 m and RF30 m(10 m and 30 m resolution images,respectively,using RF)were higher(OAs of 93.59%and 94.59%,respectively)than those of the global land cover dataset(LC10 m and LC30 m,land cover images with 10 m and 30 m resolution,respectively),whose high-resolution images showed more details of the land cover.The results of this study highlight that classification algorithms and image resolution are the sources of uncertainty for land mapping.Obtaining reliable land cover mapping requires the use of appropriate classification algorithms and spatial resolution.With these results,it will be possible to develop a national land monitoring system and basic ecological climate models using LULC.
文摘As the use of Cannabis products as natural medicines burgeons,it is also appearing as a food ingredient.It is important to screen Cannabis samples as ingredients by profiling their chemical compositions,which is referred to as chemotyping.Two sets of botanical extracts were studied.The first set is referred to as Cannabis contained plant materials from 15 samples of the sativa,indica,and hybrids of the two species.The second set contained 20 extracts from the variety of Cannabis sativa with low tetrahydrocannabinol(THC)concentrations,i.e.,below 0.3%,and,henceforth,will be referred to as hemp.An ultraviolet(UV)microplate reader provides a cost-effective and high-throughput method for identifying chemotypes of plant extracts by their spectra.The microplate reader affords rapid measurements of small volumes,e.g.,50μL,which demonstrates a potential to significantly reduce the analysis time and cost for Cannabis and hemp chemotyping or chemi-cal profiling.Replicate samples were measured on different days to demonstrate the robustness of the method.Projected difference resolution(PDR)maps were used to visualize the separations among the classes.Five multivariate classifiers,fuzzy rule-building expert system(FuRES),super partial least squares-discriminant analysis(sPLS-DA),support vector machine(SVM),and two tree-based support vector machines(SVMtreeG and SVMtreeH)were evaluated.The classifiers were validated with ten bootstrapped Latin partitions(BLPs).For the Cannabis extracts,the SVMtreeG yielded the best performance and the classification accuracy was 99.1±0.4%for spectra collected in the nonlinear absorbance range.For the hemp extracts,the SVM classifier performed the best with a 97.4±0.6%classification accuracy.These results demonstrate that the UV microplate reader coupled with multivariate classifiers can be used as a high-throughput and cost-effective approach for chemotyping Cannabis.
